In many pattern recognition problems, the main task is to match two images of an object (e.g., face, iris, vehicle, etc.) that may exhibit appearance differences due to factors such as translation, rotation, scale change, occlusion, illumination variations and others. One class of methods to achieve accurate object recognition in the presence of such appearance variations is one where features computed in a sliding window in the target image are compared to features computed in a stationary window of the reference image. Correlation filters are an efficient frequency-domain method to implement such sliding window matching. They also offer benefits such as shift-invariance (i.e., the object of interest can be off-center), no need for segmentation, graceful degradation and closed-form solutions. While the origins of correlation filters go back more than thirty years, there have been some very interesting and useful advances in correlation filter designs and their applications. For example, the new maximum margin correlation filters (MMCFs) show how the superior localization capabilities of correlation filters can be combined with the generalization capabilities of support vector machines (SVMs). Another major research advance is the development of vector correlation filters that use features (e.g., HOG) extracted from the input image rather than just input image pixel values. While past application of correlation filters focused mainly on automatic target recognition, more recent applications include face recognition, iris recognition, palmprint recognition and visual tracking. This talk will provide an overview of correlation filter designs and applications, with particular emphasis on these more recent advances. [Go to the full record in the library's catalogue]
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